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Forecasting Euro-area recessions using time-varying binary response models for financial

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  • Bellégo, C.
  • Ferrara, L.

Abstract

Recent macroeconomic evolutions during the years 2008 and 2009 have pointed out the impact of financial markets on economic activity. In this paper, we propose to evaluate the ability of a set of financial variables to forecast recessions in the euro area by using a non-linear binary response model associated with information combination. Especially, we focus on a time-varying probit model whose parameters evolve according to a Markov chain. For various forecast horizons, we provide a readable and leading signal of recession by combining information according to two combining schemes over the sample 1970-2006. First we average recession probabilities and second we linearly combine variables through a dynamic factor model in order to estimate an innovative factor-augmented probit model. Out-of-sample results over the period 2007-2008 show that financial variables would have been helpful in predicting a recession signal as September 2007, that is around six months before the effective start of the 2008-2009 recession in the euro area.

Suggested Citation

  • Bellégo, C. & Ferrara, L., 2009. "Forecasting Euro-area recessions using time-varying binary response models for financial," Working papers 259, Banque de France.
  • Handle: RePEc:bfr:banfra:259
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    Cited by:

    1. Fornari, Fabio & Lemke, Wolfgang, 2010. "Predicting recession probabilities with financial variables over multiple horizons," Working Paper Series 1255, European Central Bank.
    2. Adrian Pagan & Don Harding, 2011. "Econometric Analysis and Prediction of Recurrent Events," CREATES Research Papers 2011-33, Department of Economics and Business Economics, Aarhus University.
    3. Emmanuelle Lavallée & Vincent Vicard, 2013. "National borders matterwhere one draws the lines too," Canadian Journal of Economics, Canadian Economics Association, vol. 46(1), pages 135-153, February.
    4. Christophe Bellégo & Laurent Ferrara, 2010. "A factor-augmented probit model for business cycle analysis," EconomiX Working Papers 2010-14, University of Paris Nanterre, EconomiX.
    5. Barış Soybilgen, 2020. "Identifying US business cycle regimes using dynamic factors and neural network models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 827-840, August.
    6. Goodhead, Robert & Parle, Conor, 2019. "Predicting Recessions in the Euro Area: A Factor Approach," Economic Letters 2/EL/19, Central Bank of Ireland.
    7. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2014. "Dynamic factor models: A review of the literature," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 73-107.
    8. Carsten Jentsch & Lena Reichmann, 2019. "Generalized Binary Time Series Models," Econometrics, MDPI, Open Access Journal, vol. 7(4), pages 1-26, December.
    9. Laurent Ferrara & Cl�ment Marsilli, 2013. "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession," Applied Economics Letters, Taylor & Francis Journals, vol. 20(3), pages 233-237, February.

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    More about this item

    Keywords

    Macroeconomic forecasting; Business cycles; Turning points; Financial markets; Non-linear time series; Combining forecasts.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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